There are many problems in computer vision related to light conditions, resolution and quality of the image, contrast, noise, or motion. To some extent, multimedia analytics software such as Oracle Big Data Spatial and Graph assists with general tasks such as video frame extraction or video decoding and some specialized tasks such as facial recognition. For example, Oracle Big Data Spatial and Graph includes the Open Source Computer Vision (OpenCV) open source libraries that provide some video analysis such as template-based object recognition and optical flow.
State of the art motion detection often provides results that are poor or meaningless for semantic analysis. Current software returns a binary result such as: a) there is motion, or b) there is no motion. If there is motion, current software may do little more than crop the video segment that contains motion and display the cropped segment to the user. The state of the art also succumbs to noise that may appear as motion. For example, every camera generates noise that reduces the accuracy or efficiency of object detection and motion analysis. Thus, there is a need for better post-capture preparation of video footage to increase the accuracy and/or efficiency of object recognition and object tracking.